multiple regression analysis
statistical procedure that attempts to assess the relationship between a dependent variable andtwoor more independent variables. Examples: Total factory overhead (the dependent variable) is related to both labor-hours and machinehours (the independent variables). Sales of a popular soft drink (the dependent variable) is a function of various factors, such as its price, advertising, taste, and the prices of its major competitors (the independent variables).
See also
regression analysis
statistical procedure for estimating the average relationship between the dependent variable (sales, for example) and one or more independent variables (price and advertising, for example). It is a popularly used method for estimating the cost-volume formula(y = a + bx). simple regression involves one independent variable, e.g., direct labor-hours or machine-hours alone, whereas multiple regression involves two or more independent variables. Assuming a linear relationship, the simple regression model indicates that the relationship is y = a + bx, where a, and b are unknown constants, called regression coefficients. The multiple regression model is y = a0 + a1x1 + a2x2 + ... + akxk, where a's are coefficients and x's represent the number of independent variables.
In estimating the cost-volume formula, regression analysis attempts to find a line of best fit. To find the line of best fit, a technique called the least-squares method is widely used.
